Electronic devices may include a variety of sensors and inputs to monitor and infer relative device position. For example, based on input received by a WiFi sensor, a device can measure Received Signal Strength Indication (RSSI) or Round Trip Time (RTT) to infer device position relative to one or more wireless access points. In another example, a Global Navigation Satellite System (GNSS) can be used to determine device position. Through a combination of device measurements, Access Point (AP) locations may be determined within an environment.
Current techniques for wireless AP location mapping typically have reduced effectiveness when accurate mobile device position fixes are unavailable. For example, instead of referencing actual GNSS positions in proximity to AP measurements, typical techniques for mapping APs (for example, WiFi and/or BlueTooth (BT) APs) may calculate a weighted mean of Global Navigation Satellite System (GNSS) positions available at the times APs measurements are observed. However, relying on a weighted mean for positions can lead to less accurate indoor AP mapping when AP measurement data is associated with a number of missing GNSS signals, such as may occur indoors or when satellite reception is unreliable. Also, because traditional mobile positioning is often less accurate indoors, BT APs which may be predominantly indoors, are mapped less frequently and with less accuracy.
Another technique for determining AP locations includes AP map fingerprinting by a specialized testing engineer for each AP location. However, testing engineers may be expensive and time consuming to deploy and is not readily scalable to many locations.
Data collection servers may process and redistribute position information collected by a number of electronic devices (for example, crowdsourcing). However, always on data collection by electronic devices may consume limited device resources while providing data to the data collection server. For example, when the device is a mobile device that uses battery power, data collection can consume some of the limited battery resources of the device as one or more sensors gather data. Furthermore, reporting data out to a collection server or other device can also consume limited wireless bandwidth resources. For example, users may have a data bandwidth cap and may be charged excess usage fees when data use exceeds the cap. Additionally, clients may send erroneous data to servers and quality of data received from mobile devices may not be easily verifiable. Therefore, new and improved data collection techniques are desirable.